High-quality content generation system, method therefor, and program

ABSTRACT

A high-quality content generating system provided with a feature amount extracting means for extracting the feature amounts of a plurality of pieces of content therefrom, a content grouping means for performing matching between the feature amounts of the plurality of pieces of content extracted by the feature amount extracting means, grouping the same content included in the plurality of pieces of content and the derived content produced by using the same content, and calculating same/derived content grouping information, and a high-quality content generating means for selecting pieces of content to be grouped by the same/derived content grouping information from the plurality of pieces of content and generating content with higher quality by using the selected content.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/JP2009/060909 filed Jun. 16, 2009, claiming priority based onJapanese Patent Application No. 2008-167345 filed Jun. 26, 2008, thecontents of all of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present invention relates to a high-quality content generationsystem, and a method and a program therefor.

BACKGROUND ART

Recently, many video hosting sites are rising, and the environment inwhich various videos can be viewed via Internet is being arranged. Inthis circumstance, the identical contents such as video images forpromotion that should be positively distributed, and the identicaltopical contents are repeatedly contributed to various video hostingsites in some cases. Usually, any video site provides a function ofretrieving the contents within its own site, and recently, retrievalengines capable of retrieving the contents striding over a plurality ofthe sites are also coming onstage. Cross-sectionally searching aplurality of the video image hosting sites by using this retrievalengine allows the identical contents to be found in a plural number insome cases.

In this circumstance, it is enough for a user to find one of thecontents that the user desires to catch, thus, the user desiring to viewthe contents at a high quality has the following problem.

SUMMARY OF INVENTION Technical Problem

A first controversial problem is that it is difficult to find thehigh-quality contents.

The reason is that a possibility that moderately selecting and viewingone of the contents found in the retrieval leads to catching thelow-quality contents is high. With the case of the video hosting site,individual contents are often encoded at a high reduction rate because alarge volume of the contents are handled. For this, a possibility ofcatching the low-quality contents is high because there are manycontents of which the image quality is low in an original status. As aresult, the user desiring to view the contents at a high quality has tosearch for the high-quality one by viewing the identical contentsplurally found one by one, or has to resignedly put up with viewing ofthe contents of which the reproduction quality is poor due to timerestriction etc. notwithstanding desiring to view the high-qualitycontents when the image quality of the accidentally viewed content ispoor.

A second controversial problem is that it is difficult for the user toview the high-quality content in all sections.

The reason is that even though the retrieval allows a plurality of theidentical contents to be found, the content to be viewed by the user isonly one of them. It is not true that the image quality of a certaincontent is always excellent from beginning to end. For example, VBRcoding at a low rate causes a large quantity of buffering to beproduced, thereby making it difficult to take a control thereof. In sucha case, it is not always true that the high quality can be kept in allsections. For this, it is not usually carried out to switch the contentthat the user is viewing to the other identical content even though thelocation in which the quality declines halfway exists for the reasonthat the time and labor are required, the viewing is interrupted, or thelike. For this, even though the quality declines halfway, the user hasno chance but resignedly putting up with a decline in the quality.

Thereupon, the present invention has been accomplished in considerationof the above-mentioned problems, and an object thereof is to provide ahigh-quality content generation system capable of, when the identicalcontents and the contents derived from them exist in a plural number,generating the higher-quality contents by employing them, and a methodand a program therefor.

Solution to Problem

The present invention for solving the above-mentioned problems is ahigh-quality content generation system including: a feature extractionmeans for extracting features of contents from a plurality of thecontents; a content grouping means for collating the features of aplurality of the contents extracted by the foregoing feature extractionmeans with each other, grouping the identical contents and the derivedcontents produced by using the above identical contents to be includedin the foregoing plurality of the contents, and calculatingidentical/derived content grouping information; and a high-qualitycontent generation means for selecting the contents to be grouped withthe foregoing identical/derived content grouping information from amongthe foregoing plurality of the contents, and generating the contents ofwhich a quality is more excellent by employing the selected contents.

The present invention for solving the above-mentioned problems is ahigh-quality content generation method including: a feature extractionstep of extracting features of contents from a plurality of thecontents; a content grouping step of collating the features of theforegoing plurality of the extracted contents with each other, groupingthe identical contents and the derived contents produced by using theabove identical contents to be included in the foregoing plurality ofthe contents, and calculating identical/derived content groupinginformation; and a high-quality content generation step of selecting thecontents to be grouped with the foregoing identical/derived contentgrouping information from among the foregoing plurality of the contents,and generating the contents of which a quality is more excellent byemploying the selected contents.

The present invention for solving the above-mentioned problems is ahigh-quality content generation program for causing an informationprocessing apparatus to execute: a feature extraction process ofextracting features of contents from a plurality of the contents; acontent grouping process of collating the features of the foregoingplurality of the extracted contents with each other, grouping theidentical contents and the derived contents produced by using the aboveidentical contents to be included in the foregoing plurality of thecontents, and calculating identical/derived content groupinginformation; and a high-quality content generation process of selectingthe contents to be grouped with the foregoing identical/derived contentgrouping information from among the foregoing plurality of the contents,and generating the contents of which a quality is more excellent byemploying the selected contents.

Advantageous Effect of Invention

The present invention is capable of, when the identical contents and thecontents derived from them exist in a plural number, generating thehigher-quality contents by employing them.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating the high-quality contentgeneration system in a first exemplary embodiment.

FIG. 2 is a view illustrating one example of grouping the contents eachhaving a time axis.

FIG. 3 is a flowchart illustrating a flow of the entirety of a processof the high-quality content generation system of the first exemplaryembodiment.

FIG. 4 is a flowchart illustrating a flow of the first exemplaryembodiment of a high-quality content generation means 102.

FIG. 5 is a flowchart illustrating a flow of a second exemplaryembodiment of the high-quality content generation means 102.

FIG. 6 is a flowchart illustrating a flow of a third exemplaryembodiment of the high-quality content generation means 102.

FIG. 7 is a flowchart illustrating a flow of a fourth exemplaryembodiment of the high-quality content generation means 102.

FIG. 8 is a view illustrating a method of generating the high-qualitycontents when frame rates differ content by content.

FIG. 9 is a view illustrating a method of generating the high-qualitycontents when frame rates differ content by content.

FIG. 10 is a flowchart illustrating an operation of one exemplaryembodiment of an inter-content frame/field corresponding step S463 ofFIG. 7.

FIG. 11 is a flowchart illustrating an operation of one exemplaryembodiment of the inter-content frame/field corresponding step S463 ofFIG. 7.

FIG. 12 is a view for explaining time-spatial slice images.

FIG. 13 is a flowchart illustrating a flow of a fifth exemplaryembodiment of the high-quality content generation means 102.

FIG. 14 is a block diagram illustrating the high-quality contentgeneration system in the second exemplary embodiment.

DESCRIPTION OF EMBODIMENTS

The high-quality content generation system of the exemplary embodimentof the present invention is characterized in including: a featureextraction means (100 of FIG. 1) for extracting the features of thecontents from a plurality of the contents; a content grouping means (101of FIG. 1) for collating the features of a plurality of the contentsextracted by the feature extraction means with each other, obtaining theidentical contents and the derived contents produced by using the aboveidentical contents to be included in a plurality of the contents,grouping the identical/derived contents, and calculatingidentical/derived content grouping information; and a high-qualitycontent generation means (102 of FIG. 1) for selecting the contents tobe grouped with the foregoing identical/derived content groupinginformation from among the foregoing plurality of the contents, andgenerating the higher-quality contents by employing the selectedcontents. Employing such a configuration, grouping the identical/derivedcontents in terms of a plurality of the contents, generating thehigh-quality contents by employing these, and presenting them to theuser makes it possible to accomplish an object of the present invention.

Next, the exemplary embodiments of the present invention will beexplained in details by making a reference to the accompanied drawings.

FIG. 1 is a block diagram illustrating the high-quality contentgeneration system in the first exemplary embodiment.

Upon making a reference to FIG. 1, the high-quality content generationsystem of the first exemplary embodiment is configured of a featureextraction means 100, an identical/derived content grouping means 101, ahigh-quality content generation means 102, and a content storage means105.

The content storage means 105, which stores a plurality of the contents,is connected to the feature extraction means 100 and the high-qualitycontent generation means 102. The feature extraction means 100, intowhich the contents are inputted from the content storage means 105,obtains the features for the contents and outputs the features to theidentical/derived content grouping means 101. The identical/derivedcontent grouping means 101, into which the features of the contents tobe outputted from the feature extraction means 100 are inputted, obtainscontent link information representing a link relation between thefeatures, and outputs it as grouping information to the high-qualitycontent generation means 102. The high-quality content generation means102, into which the grouping information is inputted from theidentical/derived content grouping means 101, and the contents areinputted from the content storage means 105, respectively, generates andoutputs the high-quality contents.

Next, an operation of the high-quality content generation system of thisexemplary embodiment will be explained.

The contents are stored in the content storage means 105. Herein, theso-called content refers, for example, to a digitalized multimediacontent, and the digitalized pictures, video and music, a combinationthereof, and the like fall under the content. Additionally, the contentcould be not only a content produced by a professional such as abroadcast program, but also a so-called CGM (Consumer Generated Media),being a content produced by a consumer. Hereinafter, while the videoimage content is basically specialized for explanation, the situation isalso similarly applicable to the music, the picture, and the like.

Further, while, for convenience, the content storage means 105 wasexplained in such a manner that the contents were stored in onelocation, the contents may be dispersedly stored in a plurality of thestorages. For example, in a plurality of the video image hosting sitesover Internet, the video image contents may be stored for each site.Further, also in each site, the contents may be dispersed and stored ina plurality of the storages. The contents stored in the content storagemeans 105 are inputted into the feature extraction means 100.

The feature extraction means 100 performs the feature extraction foreach of the contents to be inputted. With the case of the picture, thefeature is a visual feature such as color, pattern, and shape, and forexample, the feature standardized by ISO/IEC 15938-3 can be employed.With the case of the music, the feature is an audio feature such as apower and a frequency component of sound, and for example, the featurestandardized by ISO/IEC 15938-4 can be employed. With case of the video,besides the foregoing visual feature, the visual feature expressive ofmotion can be also employed, and the feature standardized by ISO/IEC15938-3 can be employed. Further, the foregoing audio feature may beemployed, and both of the visual feature and the audio feature may beemployed. The extracted feature of each of the contents is outputted tothe identical/derived content grouping means 101.

The identical/derived content grouping means 101 collates the featuresof the contents to be inputted with each other, regards the contents ofwhich the similarity between the features is large as contents eachhaving identical details, and groups them. Specifically, theidentical/derived content grouping means 101 calculates the similarity(or a distance) between the features of certain two contents, and groupsthe above two contents when the similarity is equal to or more than athreshold (equal to or less than a threshold with the case of thedistance).

At the moment of calculating the similarity, with the case of thepicture, comparing the features with each other in terms of the entiretyof the picture and performing the similarity calculation makes itpossible to group the identical pictures. Further, the similarity may becalculated by collating region partners of one part of the picture witheach other. In this case, the other images that can be obtained by usinga certain picture (for example, the images that can be obtained byframing the picture, and the images that can be obtained by affixing acertain picture to another picture), namely, the derived contents can bealso grouped. On the other hand, with the case of the contents eachhaving a time axis such as the video and the music, theidentical/derived content grouping means 101 groups the contents interms of each time section (a section length is arbitrary). For example,when it is assumed that a collation between each of a content A, acontent B, a content C and a content D, and the other is carried out asshown in FIG. 2, the time section partners shown with oblique stripedlines are grouped, and the time section partners shown with verticalstriped lines are grouped. The grouping information obtained in such amanner is outputted to the high-quality content generation means 102.

The high-quality content generation means 102 generates the high-qualitycontents from the grouping information to be inputted and thecorrespondence contents. For example, with the case of an example ofFIG. 2, the high-quality content generation means 102 generates thehigh-quality contents by employing a content A, a content B, a contentC, and a content D. The details of this generation will be describedlater.

Next, an operation of the high-quality content generation system of thisexemplary embodiment will be explained by employing a flowchart.

FIG. 3 is a flowchart representing a flow of the entirety of a processof the high-quality content generation system in the first exemplaryembodiment.

At first, in a step S300, the by-content feature is extracted. Thedetails of the extraction are ones described in the feature extractionmeans 100. Next, in a step S301, the extracted features are collated interms of the content, the contents are grouped, and the groupinginformation is obtained. The details of the grouping are ones describedin the identical/derived grouping means 101. And, in a step S302, thehigh-quality contents are generated from the grouping information andthe contents.

Next, an operation of the first exemplary embodiment of the high-qualitycontent generation means 102 will be described in details while areference to figures is made.

FIG. 4 is a flowchart representing a flow of the first exemplaryembodiment of the high-quality content generation means 102.

At first, it is assumed that indexes of time sections of the content,being a target for generating the high-quality contents, are representedas i=0, 1, . . . , N−1. That is, it is assumed that the content, being atarget for generating the high-quality contents, is divided N timesections (N is an arbitrary natural number), and the high-qualitycontents are generated section by section.

In a flowchart, at first, in a step S400, i, being an index of thesection, is set as zero.

Next, in a step S401, the grouping information associated with thesection i is loaded. For example, with the case of an example of FIG. 2,information of a correspondence relation such that, when i belongs tothe part shown with oblique lines, the contents A, B, and C are groupedcorrespondingly to each other, and while no offset in a time directionexists between the contents A and B, the content C is offset by t₁ inthe time direction is obtained from the grouping information.

Next, in a step S402, it is investigated whether the correspondingcontent exists. With the case of the section in which only onecorrespondent content exists, the operation jumps to step S405 becauseit is necessary to generate the high-quality content, being an output ofthe above content alone. On the other hand, when the correspondentcontents exist like an example of FIG. 2, the operation proceeds to thenext step, namely S403.

In the step S403, a quality evaluation value in the time sectioncorresponding to the section i is calculated for each of the contentscaused to correspond to each other. Herein, the methods of calculatingthe quality evaluation value exist in a plural number, and the detailsthereof will be described later.

In the next step, namely S404, the quality evaluation values obtained inthe step S403 are compared with each other in terms of the content toselect the content of which the quality becomes highest.

In a step S405, the part corresponding to the section i of the selectedcontent is copied to a buffer for the outputting. And, the above part isencoded in an appropriate output format. Additionally, with regard tothe encoding of the above part in the output format, after generatingplural section portions of the high-quality contents, the above part maybe encoded in the output format together with them.

Next, in a step S406, it is investigated whether the section i is a lastsection, and when the section i is not a last section, an index i of thesection is increased by one (1) and the operation returns to the stepS401. When the section i is a last section, the process is finished.

In such a manner, the high-quality contents can be generated.Additionally, so far, the case that the content of which the qualitybecame highest was selected for each time section of the content and thehigh-quality contents were generated was described. The above sectionmay include one frame. That is, the quality may be evaluated frame byframe to generate the high-quality contents. Or, each frame may bedivided into a plurality of regions to change the content of which thequality becomes highest region by region by judging the quality for eachof these regions. For example, the frame may be divided into a pluralityof blocks to evaluate the quality for each block unit. This enables thefiner high-quality contents to be generated. However, in a time-spatialboundary in which a changeover to the content that is to be selected ismade, a transition process may be performed so that both are smoothlychanged in the adjacent of the boundary because an unnatural gap betweenthe content qualities occurs in some cases.

Next, the details of the method of calculating the quality evaluationvalue in the step S403 will be described.

At first, the case of calculating the quality evaluation value from abit amount to be assigned to the section i and a coding method will bedescribed. In this case, firstly, the generated code quantity in thetime section caused to correspond to the section i of each of thecorresponding content are investigated. The value that becomes larger asthe generated code quantity become larger is defined as the qualityevaluation value because, as a rule, it can be said safely that the morethe generated code quantity, the higher the image quality. However, whena coding structure of each content that is caused to correspond to thesection i differs (for example, when the number etc. of the I picturesand P pictures differs), the above difference may be taken intoconsideration at the moment of calculating the quality evaluation value.Further, when the coding method differs, the generated code quantity ofthe contents each having an identical quality differ from each other.For example, as a rule, the content encoded with H.264 is more excellentin the quality as compared with the content encoded with MPEG-1 eventhough each has the identical generated code quantity. The qualityevaluation value may reflect the characteristic caused by such adifference of the coding method. Additionally, how to control thequality evaluation value responding to a difference of the coding methodand a difference of the coding structure can be decided, for example, byencoding the identical content with the various methods andinvestigating a relation with the image quality. Needless to say, aframework of machine learning may be employed in this stage.

Next, the case of evaluating magnitude of coding noise and calculatingthe quality evaluation value will be described. In this case, the imagequality of the images that is obtained by decoding the time sectioncaused to correspond to the section i of each of the correspondingcontents is checked. For example, by employing the method of Literature1 (JP-P2005-159419A “APPARATUS AND METHOD OF PROCESSING IMAGE, COMPUTERPROGRAM AND COMPUTER-READABLE STORAGE MEDIUM”), the coding noise can bequantified.

Needless to say, the method of measuring the coding noise is not limitedhereto, and any arbitrary method, which enables the image quality to bedetermined by employing only decoded images without employing theoriginal images, may be used. At this moment, the quality evaluationvalue may be defined in such a manner that the quality evaluation valuebecomes smaller as the coding noise becomes larger, and employed.

Or, the image quality may be evaluated by employing a parameter ofquantization. Extracting the coding parameter associated with thequantization from the content makes it possible to evaluate the qualitybecause coarse quantization causes the image quality to decline as arule. At this moment, the quality evaluation value may be defined insuch a manner that the quality evaluation value becomes smaller as thequantization parameter becomes larger, and employed.

When a resolution differs between the contents caused to correspond toeach other, the quality may be evaluated by employing the resolution.The quality evaluation value may be defined in such a manner that thequality evaluation value becomes larger as the resolution becomeslarger, and employed because, as a rule, it can be said safely that thelarger the resolution, the higher the image quality.

Further, when a frame rate differs between the contents caused tocorrespond to each other, the quality may be evaluated by employing theframe rate. The quality evaluation value may be defined in such a mannerthat the quality evaluation value becomes larger as the frame ratebecomes higher, and employed because the higher the frame rate, the moresmooth the motion, which leads to an improvement in the quality of thevideo.

While the methods of obtaining the quality evaluation value based uponindividual factors were described above, the quality evaluation valueincorporating these factors in a plural number may be defined andemployed.

The above is an operation of the high-quality content generation means102 based upon a flowchart shown in FIG. 4.

Next, an operation of the second exemplary embodiment of thehigh-quality content generation means 102 will be explained in detailswhile a reference to figures is made.

FIG. 5 is a flowchart representing a flow of the process of the secondexemplary embodiment of the high-quality content generation means 102.Basically, a step S423 is included therein instead of the step S403 andthe step S404 of FIG. 4. The steps other than it are identical to thatof a flowchart of FIG. 4, so the step S423 will be described below.

In the step S423, the frame to be outputted is generated by performing astatistics process for pixel values in terms of the corresponding framesof the contents caused to correspond to each other. For example, byaveraging the values of the pixels existing in identical pixel positionsin terms of the frames of a plurality of the contents caused tocorrespond to each other, the pixel value in the above pixel position ofthe output frame is calculated. Averaging the pixel values in such amanner makes it possible to offset noise components contained in theindividual contents and to enhance the quality of the output frame.

Or, instead of employing the method of simply averaging the pixelvalues, the method may be used of obtaining the pixel value by excludingoutliers like an M-estimation method. For example, when the content ofwhich the pixel value largely differs exists, the pixel value of theoutput frame may be obtained by excluding the pixel value of the abovecontent and averaging the remaining pixel values. In this case, forexample, also when the frame is caused to correspond to the derivedcontent having telop inserted therein, the output frame can becalculated without an influence by the above telop received.

Or, the statistics process may be performed by calculating the qualityevaluation value content by content described in the explanation in thestep S403 with a flowchart of FIG. 3, or by employing only the qualityevaluation values satisfying a certain constant criteria. This canexclude an influence by the content of which the quality is extremelylow.

When the content of which the resolution differs is included, thestatistics process may be performed after carrying out an interpolationso as to meet the resolution of the output frame.

The above is an operation of the high-quality content generation means102 based upon a flowchart shown in FIG. 5.

Next, an operation of the third exemplary embodiment of the high-qualitycontent generation means 102 will be explained in details while areference to figures is made.

FIG. 6 is a flowchart representing a flow of the process of the thirdexemplary embodiment of the high-quality content generation means 102.Basically, a step S443 is included therein instead of the step S403 andthe step S404 of FIG. 4. The steps other than it are identical to thatof a flowchart of FIG. 4, so the step S443 will be described below.

In the step S443, the frame to be outputted is generated by performing asuper-resolution process in terms of the corresponding frames of thecontents caused to correspond to each other. Specifically, afterpositioning the pixels for each frame, the output frame is generated byperforming the process such as blind deconvolution and sharpening theframe.

The above is an operation of the high-quality content generation means102 based upon a flowchart shown in FIG. 6.

Next, an operation of the fourth exemplary embodiment of thehigh-quality content generation means 102 will be explained in detailswhile a reference to figures is made.

FIG. 7 is a flowchart representing a flow of the process of an operationof the fourth exemplary embodiment of the high-quality contentgeneration means 102. Basically, a step S463 and a step S464 areincluded therein instead of the step S403 and the step S404 of FIG. 4.The steps other than it are identical to that of a flowchart of FIG. 4,so the step S463 and the step S464 will be described below.

In the step S463, the frames/fields are caused to correspond to eachother in terms of the content. With the case of the content having adifferent frame rate, the correspondence relation between the framesobtained by the collation in the identical/derived content groupingmeans 101 could shift slightly because the contents do not completelycoincide with each other. For this, the correspondence relation isadjusted finely to enhance a precision of the collation. These detailsare described later.

Next, in the step S464, the output frames are generated in the sectioni. FIG. 8 shows the case that a content E and a content F are groupedand the output contents are generated from this. With the case of thecontents each having a different frame rate, as apparent from thisfigure, the number of the corresponding frames changes depending uponthe time position of the frame. Thus, with regard to the frame that isoutputted at the time when a plurality of the frames of the contents arecaused to correspond to each other, the above output frame is generatedby employing a plurality of these frames. As a method of generating theoutput frame, the various methods described above may be employed. Onthe other hand, in the time position in which a plurality of the framesof the contents are not caused to correspond to each other, namely onlyone frame exists, the above frame is defined to be the output frame. Onthe other hand, as shown in FIG. 9, there also exists the case that theframes/fields for the contents do not overlap with each other. In thiscase, the frames/fields in the respective time positions are employed asthey stand as shown in FIG. 9, and are defined as output frames. In sucha manner, the frames of the high-quality contents are generated.

Next, the details of the process for the frame/field corresponding inthe step S463 will be explained by employing a flowchart.

FIG. 10 is a flowchart representing a flow of the process of the stepS463.

At first, in a step S600, the frame rates are matched to each other interms of the content by the frame interpolation process. Specifically,the frame rate is raised to the least common multiple (for example, 30fps with the case of 10 fps and 15 fps) of the frame rates for thecontents. As a frame interpolation technique at this moment, there existthe technique of performing the linear interpolation for the temporallypre and post frames according to the temporal distance, and thetechnique of compensating motion of objects and generating theinterpolated frame from the temporally pre and post frames. At thismoment, a part of a background to be covered with the moving object anda part of a newly appearing background exist. The region to be coveredcan correspond to only the temporally pre frames, and to the contrary,the newly appearing region can correspond to only the temporally postframes. In this case, the pixel values of one region that is caused tocorrespond are employed as they stand. Additionally, with the case ofthe content that originally has an interlace structure, theabove-mentioned interpolation process is performed in the fieldstructure.

Next, in a step S601, the features of respective frames/fields areextracted. The features to be extracted at this moment could beidentical to and could be different from the features employed in thefeature extraction means 100. The features sensitive to a shift in thetime axis direction are desirably employed because the fine positioningin the time axis direction has to be carried out.

Next, in a step S602, the features are collated with each other in termsof the content to fix the corresponding of the frames/fields.Originally, the positions in the time direction are approximately causedto correspond to each other in terms of the content even though thisprocess is not performed, whereby it is enough to slightly shift theabove position pre and post and to select the position having a highestcollation score. In such a manner, also for the contents each having adifferent frame rate, the corresponding of the frames/fields is enabled.

Next, another method for the frame/field corresponding process in thestep S463 will be explained by employing a flowchart.

FIG. 11 is a flowchart representing a flow of the process of the stepS463.

At first, in a step S620, time-spatial slice images of each of thecontents are generated. The so-called time-spatial slice images, asshown in FIG. 12, are planes that can be obtained by cutting off thevideo content by a plane of which a coordinate value in a horizontaldirection is constant or a plane of which a coordinate value in avertical direction is constant when the video content is regarded asthree-dimensional data of horizontal component/vertical component/time.While, for convenience, the case of cutting off the video content by aplane of which a coordinate value in a horizontal direction is constantor a plane of which a coordinate value in a vertical direction isconstant is described, the images obtained by cutting off the videocontent by any arbitrary plane may be employed in principle so long asit is parallel to the time axis. FIG. 12 represents planes obtained bycutting off the video content by a plane of which a coordinate value ina horizontal direction is constant. Next, in a step S621, thetime-spatial slice images are subjected to the interpolation process.Analogously to the case of the step S600, the interpolation in the timedirection is carried out by the least common multiple (for example, 30fps with the case of 10 fps and 15 fps) of the frame rates for thecontents.

Next, in a step S622, global motion estimation considering only parallelmotion in the time direction is carried out for the time-spatial sliceimages subjected to the interpolation process in terms of the content.At this time, the method of a block matching base may be employed, andthe method such as Hough Transform may be employed. This allows amountof displacement in the time direction of the time-spatial image to beobtained. The frames/fields may be caused to correspond to each otherfor the contents according to this value.

The above is an operation of the high-quality content generation means102 based upon a flowchart shown in FIG. 7.

Next, an operation of the fifth exemplary embodiment of the high-qualitycontent generation means 102 will be explained in details while areference to figures is made.

FIG. 13 is a flowchart representing a flow of the process of the fifthexemplary embodiment of the high-quality content generation means 102.Basically, a step S483 and a step S484 are included therein instead ofthe step S403 and the step S404 of FIG. 4. The steps other than it areidentical to that of a flowchart of FIG. 4, so the step S483 and thestep S484 will be described below.

In the step S483, analogously to the step S620, the time-spatial sliceimages of each of the contents are generated. And, the high-qualitytime-spatial slice images are generated by performing thesuper-resolution process for these time-spatial slice images.

Next, in a step S484, sampling the high-quality time-spatial sliceimages at the time positions of the output frames allows the outputframes to be generated. The pixel values of the output frames areobtained only on a certain straight line equivalent to the cut end fromone high-quality time-spatial slice image. For this, the high-qualitytime-spatial slice images corresponding to arbitrary straight linesparallel to this straight line are generated, and the pixel values ofthe output frames are calculated. With this, the output frames of thehigh-quality images are generated.

The above is an operation of the high-quality content generation means102 based upon a flowchart shown in FIG. 13.

While the operation of the high-quality content generation means 102 wasexplained above, needless to say, the foregoing methods may be combinedand employed. For example, the image qualities of the high-qualitycontents prepared with a plurality of the foregoing methods may becompared with each other to select the best one. Or, the technique to beemployed may be changed section by section or region by region.

An effect of this exemplary embodiment will be explained.

The user can view the high-quality contents even though the user itselfdoes not search for the high-quality contents because this exemplaryembodiment is configured to automatically group a plurality of theidentical contents and to generate the high-quality contents. Further,with the case of the content having a time axis, the user can view thehigh-quality content for the entirety of the content without takinglabor and time such that the user views the content while making aswitchover to the high-quality content one by one because this exemplaryembodiment is configured to determine the quality of the contents judgedto be identical section by section and to select the content having abest excellent quality, or to generate the highest-quality one.

Next, the second exemplary embodiment will be explained in details bymaking a reference to the accompanied drawings.

Upon making a reference to FIG. 14, the high-quality content generationsystem of the second exemplary embodiment is configured of a featureextraction means 100, an identical/derived content grouping means 101, ahigh-quality content generation means 102, a correspondence relationmodification means 150, and a content storage means 105. The contentstorage means 105, which stores a plurality of the contents, isconnected to the feature extraction means 100 and the high-qualitycontent generation means 102.

The feature extraction means 100, into which the contents are inputtedfrom the content storage means 105, obtains the features for thecontents and outputs the features to the identical/derived contentgrouping means 101.

The identical/derived content grouping means 101, into which thefeatures of the contents to be outputted from the feature extractionmeans 100 are inputted, obtains content link information representing alink relation between the features, and outputs it as groupinginformation to the correspondence relation modification means 150.

The correspondence relation modification means 150, into which thecontents are inputted from the content storage means 105 and thegrouping information is inputted from the identical/derived contentgrouping means 101, modifies the content link information to be includedin the grouping information, and outputs the modified groupinginformation to the high-quality content generation means 102.

The high-quality content generation means 102, into which the groupinginformation is inputted from the identical/derived content groupingmeans 101, and the contents are inputted from the content storage means105, respectively, generates and outputs the high-quality contents.

Next, an operation of the high-quality content generation system of thesecond exemplary embodiment 1 will be explained.

An operation of the means other than the correspondence relationmodification means 150 is similar to that of the first exemplaryembodiment shown in FIG. 1. However, the second exemplary embodimentdiffers from the first exemplary embodiment only in a point that thehigh-quality content generation means 102 performs the process byemploying the grouping information to be outputted not from theidentical/derived content grouping means 101 but from the correspondencerelation modification means 150.

The correspondence relation modification means 150 modifies thecorrespondence relation between the contents to be outputted from theidentical/derived content grouping means 101. The reason is that thelink relation obtained by collating the features with each other couldshift from a correct correspondence relation slightly (several frames orso) in some cases. Specifically, the correspondence relationmodification means 150 makes a collation again between the contents withthe correspondence relation to be outputted from the identical/derivedcontent grouping means 101 taken as a basis, and amends this shift. Asthis method, the method described in the explanation of the step S463 ofa flowchart of FIG. 7 may be employed. The grouping informationincluding the modified correspondence relation is outputted to thecorrespondence relation modification means 150.

In the second exemplary embodiment, also when a slight error occurs dueto the collation of the features with each other, amending thecorrespondence relation makes it possible to surely generate thehigh-quality contents.

Additionally, while each part of the high-quality content generationsystem was configured with hardware in the above-mentioned exemplaryembodiments, it may be configured with the information processingapparatus that operates under a program. In this case, the programcauses the information processing apparatus to execute theabove-described operation of each part.

In accordance with this exemplary embodiment, when a plurality of thecontents has been contributed, the user can view the high-qualitycontents even though the user itself does not search for them. Thereason is that the high-quality contents are generated by employing aplurality of the contributed identical contents and are presented to theuser. This enables the user to view the high-quality contents withouthaving a hard time.

Further, this exemplary embodiment enables the user to view thehigh-quality contents from beginning to end. The reason is thatdetermining the quality of the content section by section, and selectingone having a highest quality or generating the high-quality one from aplurality of the identical contents allows the high-quality of thecontent to be realized as a whole, and the high-quality content to bepresented to the user. For this, the user can view the high-qualitycontent for the entirety of the content without taking labor and timesuch that the user views the content while making a switchover to thehigh-quality content one by one.

Above, although the exemplary embodiment has been described, the firstmode of the present invention is characterized in that a high-qualitycontent generation system comprising: a feature extraction means forextracting features of contents from a plurality of the contents; acontent grouping means for collating the features of a plurality of thecontents extracted by said feature extraction means with each other,grouping the identical contents and the derived contents produced byusing the above identical contents to be included in said plurality ofthe contents, and calculating identical/derived content groupinginformation; and a high-quality content generation means for selectingthe contents to be grouped with said identical/derived content groupinginformation from among said plurality of the contents, and generatingthe contents of which a quality is more excellent by employing theselected contents.

The second mode of the present invention, in the above-mentioned mode,is characterized in that said content has a time axis; and said contentgrouping means groups the identical/derived contents for each timesection by said collation, and calculates said identical/derived contentgrouping information; and said high-quality content generation meansgenerates the contents of which a quality is more excellent time sectionby time section by employing said selected contents.

The third mode of the present invention, in the above-mentioned mode, ischaracterized in that said content is music or video.

The fourth mode of the present invention, in the above-mentioned mode,is characterized in that said feature of the content includes at leastone of a visual feature and an audio feature.

The fifth mode of the present invention, in the above-mentioned mode, ischaracterized in that said high-quality content generation meansevaluates the quality of said selected contents time section by timesection, compares the qualities with each other in terms of the timesection corresponding to the identical section of said selected contentpartners, and selects and pieces together the time sections of thehigh-quality contents, thereby to generate the contents of which aquality is more excellent.

The sixth mode of the present invention, in the above-mentioned mode, ischaracterized in that said high-quality content generation meansdetermines the evaluation of the quality for each time section by acoding bit amount to be assigned to the above time section, and a codingtechnique.

The seventh mode of the present invention, in the above-mentioned mode,is characterized in that said high-quality content generation meansdetermines the evaluation of the quality for each time section by anevaluation index for evaluating magnitude of coding noise.

The eighth mode of the present invention, in the above-mentioned mode,is characterized in that said high-quality content generation meansevaluates the quality of said selected contents for each time sectionand for each region within a frame, compares the qualities with eachother in terms of the time section corresponding to the identicalsection of said selected content partners, and selects and piecestogether the time sections and the regions within the frame of thehigh-quality contents, thereby to generate the contents of which aquality is more excellent.

The ninth mode of the present invention, in the above-mentioned mode, ischaracterized in that said high-quality content generation meansdetermines the evaluation of the quality for each region within theframe by a coding bit amount to be assigned to the above region withinthe frame, and a coding method.

The tenth mode of the present invention, in the above-mentioned mode, ischaracterized in that said high-quality content generation meansdetermines the evaluation of the quality for each region within theframe by an evaluation index for evaluating magnitude of coding noise.

The eleventh mode of the present invention, in the above-mentioned mode,is characterized in that said evaluation index for evaluating magnitudeof the coding noise is a coding parameter for specifying coarseness ofquantization.

The twelfth mode of the present invention, in the above-mentioned mode,is characterized in that said high-quality content generation meansgenerates the contents of which a quality is more excellent by employingthe content having a highest resolution when resolutions of saidselected contents differ from each other.

The thirteenth mode of the present invention, in the above-mentionedmode, is characterized in that said high-quality content generationmeans generates the contents of which a quality is more excellent byperforming a statistics process for pixel values in terms of thecorresponding frames and generating the high-quality frames.

The fourteenth mode of the present invention, in the above-mentionedmode, is characterized in that said high-quality content generationmeans generates the contents of which a quality is more excellent bygenerating high-quality frames by employing a super-resolutiontechnology for corresponding frames when resolutions of said selectedcontents differ from each other.

The fifteenth mode of the present invention, in the above-mentionedmode, is characterized in that said high-quality content generationmeans generates the contents of which a quality is more excellent byemploying the content having a highest frame rate when frame rates ofsaid selected contents differ from each other.

The sixteenth mode of the present invention, in the above-mentionedmode, is characterized in that, when frame rates of said selectedcontents differs from each other, said high-quality content generationmeans generates the contents of which a quality is more excellent by, ina case where frames or fields that correspond between said selectedcontents exist in a plural number, selecting the frame or the fieldhaving a most excellent quality, out of them, or performing a statisticprocess for said corresponding frames or fields, thereby to generate theframes or the field, and by, in a case where frames or fields thatcorrespond between said selected contents do not exist in a pluralnumber, selecting the above frame or field.

The seventeenth mode of the present invention, in the above-mentionedmode, is characterized in that said high-quality content generationmeans determines the evaluation of the quality for each frame or fieldby an evaluation index for evaluating magnitude of coding noise.

The eighteenth mode of the present invention, in the above-mentionedmode, is characterized in that, when frame rates of said selectedcontents differs from each other, said high-quality content generationmeans generates the contents of which a quality is more excellent afteradjusting the frames or fields caused to correspond to each other forsaid contents by generating time-spatial slice images for each of saidselected contents, and estimating motion components parallel to a timeaxis direction for the corresponding time-spatial slice images in termsof said selected content.

The nineteenth mode of the present invention, in the above-mentionedmode, is characterized in that, when frame rates of said selectedcontents differs from each other, said high-quality content generationmeans generates the contents of which a frame rate is high by generatingtime-spatial slice images for each of said selected contents, generatingthe high-quality time-spatial slice images by employing asuper-resolution technology for the corresponding time-spatial sliceimages in terms of said selected content, and re-sampling saidhigh-quality time-spatial slice images.

The twentieth mode of the present invention, in the above-mentionedmode, is characterized in that the high-quality content generationsystem comprising a correspondence relation modification means forselecting from among said plurality of the contents the contents to begrouped with the identical/derived content grouping information to beoutputted from said content grouping means, modifying a correspondencerelation of the section in terms of said selected content based uponsaid identical/derived content grouping information, and outputtingmodified identical/derived content grouping information, wherein saidhigh-quality content generation means generates has said modifiedidentical/derived content grouping information as an input instead ofsaid identical/derived content grouping information.

The twenty-first mode of the present invention is characterized in thata high-quality content generation method comprising: a featureextraction step of extracting features of contents from a plurality ofthe contents; a content grouping step of collating the features of saidplurality of the extracted contents with each other, grouping theidentical contents and the derived contents produced by using the aboveidentical contents to be included in said plurality of the contents, andcalculating identical/derived content grouping information; and ahigh-quality content generation step of selecting the contents to begrouped with said identical/derived content grouping information fromamong said plurality of the contents, and generating the contents ofwhich a quality is more excellent by employing the selected contents.

The twenty-second mode of the present invention, in the above-mentionedmode, is characterized in that said content has a time axis; and saidcontent grouping step groups the identical/derived contents for eachtime section by said collation, and calculates said identical/derivedcontent grouping information; and said high-quality content generationstep generates the contents of which a quality is more excellent timesection by time section by employing said selected contents.

The twenty-third mode of the present invention, in the above-mentionedmode, is characterized in that the high-quality content generationmethod comprising a correspondence relation modification step ofselecting from among said plurality of the contents the contents to begrouped with the identical/derived content grouping information to becalculated in said content grouping step, modifying a correspondencerelation of the section in terms of said selected content based uponsaid identical/derived content grouping information, and calculatingmodified identical/derived content grouping information, wherein saidhigh-quality content generation step employs said modifiedidentical/derived content grouping information instead of saididentical/derived content grouping information.

The twenty-fourth mode of the present invention is characterized in thata high-quality content generation program for causing an informationprocessing apparatus to execute: a feature extraction process ofextracting features of contents from a plurality of the contents; acontent grouping process of collating the features of said plurality ofthe extracted contents with each other, obtaining the identical contentsand the derived contents produced by using the above identical contentsto be included in said plurality of the contents, grouping theidentical/derived contents, and calculating identical/derived contentgrouping information; and a high-quality content generation process ofselecting the contents to be grouped with said identical/derived contentgrouping information from among said plurality of the contents, andgenerating the contents of which a quality is more excellent byemploying the selected contents.

The twenty-fifth mode of the present invention, in the above-mentionedmode, is characterized in that said content has a time axis; whereinsaid content grouping process groups the identical/derived contents foreach time section by said collation, and calculates saididentical/derived content grouping information; and wherein saidhigh-quality content generation process generates the contents of whicha quality is more excellent time section by time section by employingsaid selected contents.

The twenty-sixth mode of the present invention, in the above-mentionedmode, is characterized in that said high-quality content generationprogram causing the information processing apparatus to execute acorrespondence relation modification process of selecting from amongsaid plurality of the contents the contents to be grouped with theidentical/derived content grouping information to be calculated in saidcontent grouping process, modifying a correspondence relation of thesection in terms of said selected content based upon saididentical/derived content grouping information, and calculating modifiedidentical/derived content grouping information, wherein saidhigh-quality content generation process employs said modifiedidentical/derived content grouping information instead of saididentical/derived content grouping information.

Above, although the present invention has been particularly describedwith reference to the preferred embodiments and modes thereof, it shouldbe readily apparent to those of ordinary skill in the art that thepresent invention is not always limited to the above-mentionedembodiment and modes, and changes and modifications in the form anddetails may be made without departing from the spirit and scope of theinvention.

This application is based upon and claims the benefit of priority fromJapanese patent application No. 2008-167345, filed on Jun. 26, 2008, thedisclosure of which is incorporated herein in its entirety by reference.

INDUSTRIAL APPLICABILITY

The present invention is applicable to fields such as a system and aprogram of generating the high-quality contents by employing thecontents accessible from the network, and presenting them to the user.Further, the foregoing field is not limited to the network, and thepresent invention is similarly applicable to the contents stored in anidentical hard disc recorder so long as the identical/derived contentsexist in it.

REFERENCE SIGNS LIST

-   -   100 feature extraction means    -   101 identical/derived content grouping means    -   102 high-quality content generation means    -   105 content storage means    -   150 correspondence relation modification means

The invention claimed is:
 1. A high-quality content generation systemcomprising: a feature extractor configured to extract features ofcontents from a plurality of the contents; a content grouping unitconfigured to collate the features of a plurality of the contentsextracted by said feature extractor, to group identical contents withderived contents produced by using the identical contents to be includedin said plurality of the contents, and to calculate identical/derivedcontent grouping information; and a high-quality content generatorconfigured to select the contents to be grouped with saididentical/derived content grouping information from among said pluralityof the contents, and to generate contents for which a quality isimproved by employing the selected contents, wherein the contentscomprise at least one from among digitalized multimedia content, picturecontent, and video content.
 2. A high-quality content generation systemaccording to claim 1: wherein each of said contents is associated with arespective time section on a time axis; wherein said content groupingunit is further configured to group the identical/derived contents foreach time section by said collation, and to calculate saididentical/derived content grouping information; and wherein saidhigh-quality content generator is further configured to generate thecontents for which the quality is improved for each time section byemploying said selected contents.
 3. A high-quality content generationsystem according to claim 2, wherein said contents include at least onefrom among music and video.
 4. A high-quality content generation systemaccording to claim 3, wherein said features of said contents includes atleast one of a visual feature and an audio feature.
 5. A high-qualitycontent generation system according to claim 2, wherein saidhigh-quality content generator is further configured to evaluate aquality of said selected contents for each respective time section, tocompare the evaluated quality for each respective time section with anevaluated quality of a corresponding time section which has been groupedas having identical contents of said selected contents, and to selectand combine the time sections of the high-quality contents based on aresult of the comparing.
 6. A high-quality content generation systemaccording to claim 5, wherein said high-quality content generator isfurther configured to determine the evaluation of the quality for eachrespective time section by using a respective coding bit amount to beassigned to the respective time section, and to determine acorresponding coding technique.
 7. A high-quality content generationsystem according to claim 5, wherein said high-quality content generatoris further configured to determine the evaluation of the quality foreach respective time section by using an evaluation index for evaluatinga magnitude of coding noise.
 8. A high-quality content generation systemaccording to claim 2, wherein said high-quality content generator isfurther configured to evaluate a quality of said selected contents foreach respective time section and for each region within a frame, tocompare the evaluated quality for each respective time section with anevaluated quality of a corresponding time section which has been groupedas having identical contents of said selected contents, and to selectand combine the time sections and the regions within the frame of thehigh-quality contents based on a result of the comparing.
 9. Ahigh-quality content generation system according to claim 8, whereinsaid high-quality content generator is further configured to determinethe evaluation of the quality for each region within the frame by usinga respective coding bit amount to be assigned to the respective regionwithin the frame, and to determine a corresponding coding method.
 10. Ahigh-quality content generation system according to claim 8, whereinsaid high-quality content generator is further configured to determinethe evaluation of the quality for each region within the frame by usingan evaluation index for evaluating a magnitude of coding noise.
 11. Ahigh-quality content generation system according to claim 7, whereinsaid evaluation index for evaluating magnitude of the coding noiseincludes a coding parameter for specifying a coarseness of quantization.12. A high-quality content generation system according to claim 2,wherein said high-quality content generator is further configured togenerate the contents for which the quality is improved by employing acontent having a highest resolution when resolutions of said selectedcontents vary.
 13. A high-quality content generation system according toclaim 2, wherein said high-quality content generator is furtherconfigured to generate the contents for which the quality is improved byperforming a statistics process for pixel values with respect to thecorresponding frames and to generate the high-quality frames based on aresult of the performing the statistics process.
 14. A high-qualitycontent generation system according to claim 2, wherein saidhigh-quality content generator is further configured to generate thecontents for which the quality is improved by generating high-qualityframes by employing a super-resolution technology for correspondingframes when resolutions of said selected contents vary.
 15. Ahigh-quality content generation system according to claim 2, whereinsaid high-quality content generator is further configured to generatethe contents for which the quality is improved by employing a contenthaving a highest frame rate when frame rates of said selected contentsvary.
 16. A high-quality content generation system according to claim 2,wherein, when frame rates of said selected contents vary, saidhigh-quality content generator is further configured to generate thecontents for which the quality is improved by, in a case where frames orfields that correspond between said selected contents exist in a pluralnumber, selecting, from said corresponding frames or fields, a frame ora field having a highest quality, or performing a statistics process forsaid corresponding frames or fields in order to generate the frame orthe field, and by, in a case where frames or fields that correspondbetween said selected contents do not exist in a plural number,selecting the frame or the field having the highest quality from amongsaid corresponding frames or fields.
 17. A high-quality contentgeneration system according to claim 16, wherein said high-qualitycontent generator is further configured to determine the evaluation ofthe quality for each frame or field by using an evaluation index forevaluating a magnitude of coding noise.
 18. A high-quality contentgeneration system according to claim 16, wherein, when frame rates ofsaid selected contents vary, said high-quality content generator isfurther configured to generate the contents or which the quality isimproved after adjusting the corresponding frames or fields for saidcontents by generating time-spatial slice images for each of saidselected contents, and by estimating motion components parallel to atime axis direction for the corresponding time-spatial slice images withrespect to said selected contents.
 19. A high-quality content generationsystem according to claim 2, wherein, when frame rates of said selectedcontents vary, said high-quality content generator is further configuredto generate contents for which a frame rate is high by generatingtime-spatial slice images for each of said selected contents, generatinghigh-quality time-spatial slice images by employing a super-resolutiontechnology for the corresponding time-spatial slice images with respectto said selected contents, and re-sampling said high-qualitytime-spatial slice images.
 20. A high-quality content generation systemaccording to claim 2, further comprising a correspondence relationmodification unit configured to select, from among said plurality of thecontents, the contents to be grouped with the identical/derived contentgrouping information calculated by said content grouping unit, to modifya correspondence relation of each time section with respect to saidselected contents based upon said identical/derived content groupinginformation, and to output modified identical/derived content groupinginformation, wherein said high-quality content generator is furtherconfigured to generate said modified identical/derived content groupinginformation.
 21. A high-quality content generation method comprising:extracting features of contents from a plurality of contents; collatingthe extracted features, grouping identical contents and derived contentsproduced by using the identical contents to be included in saidplurality of the contents, and calculating identical/derived contentgrouping information; and selecting the contents to be grouped with saididentical/derived content grouping information from among said pluralityof the contents, and generating contents for which a quality is improvedby employing the selected contents, wherein the contents comprise atleast one from among digitalized multimedia content, picture content,and video content.
 22. A high-quality content generation methodaccording to claim 21: wherein each of said contents is associated witha respective time section on a time axis; wherein the grouping includesgrouping the identical/derived contents for each time section by saidcollation, and calculating said identical/derived content groupinginformation; and wherein the generating includes generating the contentsfor which the quality is improved for each time section by employingsaid selected contents.
 23. A high-quality content generation methodaccording to claim 21, further comprising: selecting, from among saidplurality of the contents, the contents to be grouped with thecalculated identical/derived content grouping information, modifying acorrespondence relation of each time section with respect to saidselected contents based upon said identical/derived content groupinginformation, and calculating modified identical/derived content groupinginformation.
 24. A non-transitory computer readable storage mediumstoring a high-quality content generation program for causing aninformation processing apparatus to execute a method comprising:extracting features of contents from a plurality of contents; collatingthe extracted features, obtaining identical contents and derivedcontents produced by using the identical contents to be included in saidplurality of the contents, grouping the obtained identical contents andthe obtained derived contents, and calculating identical/derived contentgrouping information; and selecting the contents to be grouped with saididentical/derived content grouping information from among said pluralityof the contents, and generating contents for which a quality is improvedby employing the selected contents, wherein the contents comprise atleast one from among digitalized multimedia content, picture content,and video content.
 25. A non-transitory computer readable storage mediumstoring a high-quality content generation program according to claim 24:wherein each of said contents is associated with a respective timesection on a time axis; wherein the grouping includes grouping theobtained identical contents and the obtained derived contents for eachtime section by said collation, and calculating said identical/derivedcontent grouping information; and wherein the generating includesgenerating the contents for which the quality is improved for each timesection by employing said selected contents.
 26. A non-transitorycomputer readable storage medium storing a high-quality contentgeneration program according to claim 24, wherein the method furthercomprises: selecting, from among said plurality of the contents, thecontents to be grouped with the calculated identical/derived contentgrouping information, modifying a correspondence relation of each timesection with respect to said selected contents based upon saididentical/derived content grouping information, and calculating modifiedidentical/derived content grouping information.